Real Interest Rates, Bubbles and Monetary Policy in the GCC countries

The Gulf Cooperation Council countries (GCC) include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. Their monetary policy objective is to stabilize the foreign price, i.e., exchange rate instead of the domestic price level, where the nominal interest rate is equalized with the US federal fund rate, but the inflation rates are independent. High oil prices and the depreciating US dollar caused inflation to rise and real interest rates to be persistently negative in the UAE and Qatar. Asset prices bubbles formed then burst creating large loses. They could have moderated the effect of, or avoided, the bubble had they floated the currency and stabilized domestic prices.Inflation, real interest rate, bubbles.

Real Interest Rates, Bubbles and Monetary Policy in the GCC countries

The Gulf Cooperation Council countries (GCC) include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. Their monetary policy objective is to stabilize the foreign price, i.e., exchange rate instead of the domestic price level, where the nominal interest rate is equalized with the US federal fund rate, but the inflation rates are independent. High oil prices and the depreciating US dollar caused inflation to rise and real interest rates to be persistently negative in the UAE and Qatar. Asset prices bubbles formed then burst creating large loses. They could have moderated the effect of, or avoided, the bubble had they floated the currency and stabilized domestic prices

Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively

Effects of fish and prawn culture on physico-chemical parameters of water and rice yield in rice fields

An experiment was conducted with five treatments i.e. rice combined with fish having regular urea fertilization (T1), rice combined with prawn having regular urea fertilization (T2), rice combined with fish with supplementary feeding (T3), rice combined with prawn with supplementary feeding (T4) and without fish and prawn (T5) was kept as control. The dissolved oxygen values obtained in treatments with fish both in morning and afternoon were lower than the values of prawn containing treatments and control. The values of nitrate-N, ammonia-N, phosphate-P and chlorophyll-a were higher in fish containing treatments than the prawn containing treatments and control. Between the two fish containing treatments the higher gross (539.44 kg/ha) and net (440.14 kg/ha) yield were obtained in T3 with supplementary feeding and the lower gross (424.88 kg/ha) and net (314.32 kg/ha) yield were recorded in T1 without supplementary feeding. Again, between two prawn containing treatments the higher gross (108.69 kg/ha) and net (81.92 kg/ha) yield were obtained in T4 with supplementary feeding and lower gross (64.32 kg/ha) and net (30.98 kg/ha) yield were recorded in T2 without supplementary feeding. The highest yield of rice grain (3.45 mt/ha) and straw (6.37 mt/ha) were obtained in T1 with fish having urea fertilization without feeding

Refining Parkinson’s neurological disorder identification through deep transfer learning

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work

Preparation and Characterization of Barium Titanate Nano Particles Using Solution Combustion

Ferroelectric materials are gaining increased importance as a result of their high dielectric constants making them useful for electrical capacitors, high piezoelectric constants to make sensors, actuators, RF Filters, ferroelectric hysteresis suit making non-volatile memories, high pyroelectric properties for infra-red detectors, thermistors, and strong electro-optic effects to be used in optical switches, data storage, etc. Barium titanate undergoes change in shape from perovskite into cubic structure Curie temperature which causes polarization, or spontaneous, polarization. Many techniques were described for the synthesis of Barium titanate including solid-state reaction, sol-gel method, hydrothermal and solution combustion. Solution combustion. The last method offers good control of the properties to meet specific requirements of the products and allows the preparation of nanomaterials  to suit energy saving and protection of environment made it attractive for many purposes. In the present work barium titanate is synthesized as submicro to nano sized particles using the solution combustion technique utilizing urea and glycerin as fuel / oxidant mixture. The experimental parameters were varied to suit the optimization of the process

Power generation from waste of IC engines

Several methods for waste thermal energy recovery from internal combustion engine (ICE) have been studied by using supercharger or turbocharger and /or combined. This study presents an innovative approach on power generation from waste of IC engine based on coolant and exhaust. The waste energy harvesting system of coolant (weHSc) is used to supply hot air at temperatures in the range of 60–70 C directly into the engine cylinder, which would be useful to vaporize the fuel into the cylinder. The waste energy harvesting system of exhaust system (weHSex) has been developed with integrating fuzzy intelligent controlled Micro-Faucet emission gas recirculation (MiF-EGR) and thermoelectric generator (TEG). In this study the MiF-EGR (micro-facet exhaust gas recirculation) will be used to maintain the intake temperature 70 C by keeping flow of the exhaust to the engine cylinder chamber and to increase the engine volumetric efficiency. The TEG produces electrical power from heat flow across a temperature gradient of exhaust and delivers DC electrical power to the vehicle electrical system which could reduce the load of the alternator by as much as 10%. The performance of weHS equipped engine has been investigated by using GT suite software for optimum engine speed of 4000 rpm. The result shows that specific fuel consumption of engine has improved by 3% due to reduction of HC formation into the engine combustion chamber causes significantly improved the emission. While, the brake power has been increased by 7% due to the fuel atomization and vaporization at engine intake temperature 70 C

Urinary tract infection and their risk factors association in renal transplant recipients

Background: Urinary tract infection (UTI) remains one of the most common and major complications after renal transplantation. Objective: The study was undertaken to get an insight regarding the bacterial pathogen which is responsible for UTI in post renal transplant patients and their risk factors association. Methods: This was an observational study, conducted in the Department of Microbiology and Immunology Bangabandhu Sheikh Mujib Medical University (BSMMU) from December 2010 to December 2011. Twenty- one renal transplant recipients were evaluated for UTl after surgery up to six weeks. Microscopic examination and culture of urine were performed in every pre-transplant period, 3rd POD, 7th POD, within six weeks and as per patient's clinical condition. UTI was considered when bacterial count wa

Dead on arrival in a low-income country: results from a multicenter study in Pakistan

BACKGROUND: This study assessed the characteristics of dead on arrival (DOA) patients in Pakistan. METHODS: Data about the DOA patients were extracted from Pakistan National Emergency Department Surveillance study (Pak-NEDS). This study recruited all ED patients presenting to seven tertiary care hospitals during a four-month period between November 2010 and March 2011. This study included patients who were declared dead-on-arrival by the ED physician. RESULTS: A total of 1,557 DOA patients (7 per 1,000 visits) were included in the Pak-NEDS. Men accounted for two-thirds (64%) of DOA patients. Those aged 20-49 years accounted for about 46% of DOA patients. Nine percent (n = 72) of patients were brought by ambulance, and most patients presented at a public hospital (80%). About 11% of DOA patients had an injury. Factors significantly associated (p \u3c 0.05) with ambulance use were men (adjusted odds ratio [aOR] = 2.72), brought to a private hospital (OR = 2.74), and being injured (aOR = 1.89). Cardiopulmonary resuscitation (CPR) was performed on 6% (n = 42) of patients who received treatment. Those brought to a private hospital were more likely to receive CPR (aOR = 2.81). CONCLUSION: This study noted a higher burden of DOA patients in Pakistan compared to other resourceful settings (about 1 to 2 per 1,000 visits). A large proportion of patients belonging to productive age groups, and the low prevalence of ambulance and CPR use, indicate a need for improving the prehospital care and basic life support training in pakistan